Prediction of Device Characteristics of Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning

Abstract

In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML). The full current–voltage (I-V) curves in forward and reverse voltage sweeps were predicted well, with high R-squared values of 0.9938 and 0.9953, respectively, by using random forest regression. Moreover, the TCAD-ML model provided high prediction accuracy not only for the full I-V curves but also for the important device features, such as the latch-up and latch-down voltages, saturation drain current, and memory window. Therefore, this study demonstrated that the TCAD-ML model can substantially reduce the computational time for device development compared with conventional simulation methods.

Convolutional Machine Learning Method for Accelerating Nonequilibrium Green’s Function Simulations in Nanosheet Transistor

“…Color versions of one or more of the figures in this letter are availableonline at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/XXXXXXXXXX nology Computer-Aided Design (TCAD) device simulations with a convolutional autoencoder (CAE) [5]- [7]. To test our idea, we have developed a new simulation approach based on the combination of TCAD and ML.…”